101. AI Agents: When LLMs Start Taking Actions
The article discusses the evolution of AI agents, highlighting their ability to take actions based on goals rather than just responding to messages. It contrasts traditional reactive systems with agents that can dynamically decide their next steps and adjust their plans accordingly. The piece emphasizes the complexity and potential of AI agents in achieving specified objectives through a series of tool interactions and decision-making processes.
- ▪AI agents operate by receiving a goal and determining the steps necessary to achieve it.
- ▪Unlike traditional systems, agents can make dynamic decisions and adjust their actions based on observations.
- ▪The article outlines the five properties of an agent, including perception, reasoning, action, memory, and goal orientation.
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Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/yakhilesh/101-ai-agents-when-llms-start-taking-actions-3pk6 |
| Publication time | Fri, 29 May 2026 11:03:32 +0000 |
| Retrieval time | 2026-05-29T11:20:00.374Z |
| Last seen | 2026-05-29T11:20:00.374Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | F37wsX-k7XAK |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1358056) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Akhilesh Posted on May 29 101. AI Agents: When LLMs Start Taking Actions #ai #python #programming #beginners Everything you have built so far is reactive. User sends a message. System processes it. System sends a response. Done. An agent is different. An agent receives a goal, not a message. It decides what steps to take to achieve that goal. It uses tools. It observes the results. It adjusts its plan.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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